基于机器学习的电动汽车电池系统的风险预警

doi:10.16265/j.cnki.issn1003-3033.2023.02.1289

中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (2): 159-165.doi: 10.16265/j.cnki.issn1003-3033.2023.02.1289

基于机器学习的电动汽车电池系统的风险预警

何淑波¹(), 项薇¹^,²^,³, 石钟淼¹

¹ 宁波大学机械工程与力学学院,浙江宁波 315211
² 浙江省零件轧制成形技术研究重点实验室,浙江宁波 315211
³ 宁波大学先进储能技术与装备研究院,浙江宁波 315211

收稿日期:2022-10-27 修回日期:2022-12-12 出版日期:2023-02-28 发布日期:2023-08-28
作者简介:
何淑波 (1995—),男,江西吉安人,硕士研究生,主要研究方向为新能源车动力电池风险评估及故障预测。E-mail:he20200710@163.com。
项薇，教授
基金资助:
宁波市自然科学基金资助(202003N4154)

Risk early warning of electric vehicle battery system based on machine learning

HE Shubo¹(), XIANG Wei¹^,²^,³, SHI Zhongmiao¹

¹ School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo Zhejiang 315211, China
² Zhejiang Provincial Key Laboratory of Part Rolling Technology, Ningbo Zhejiang 315211, China
³ Institute of Advanced Energy Storage Technology and Equipment, Ningbo University, Ningbo Zhejiang 315211, China

Received:2022-10-27 Revised:2022-12-12 Online:2023-02-28 Published:2023-08-28

摘要/Abstract

摘要：

为提高动力电池在实车工况下安全预警的及时性和准确性,将电池系统安全预警问题提炼为关键状态预测及基于预测状态的预警分类2大科学问题,根据实车运行中的电池状态数据,选择电池的单体电压最高值、单体电压极差等作为关键预测对象;利用费舍尔计分和最大信息系数(MIC)进行特征选择,采用样本卷积和交互网络模型(SCINet)实现关键状态预测;基于预测的状态,建立多分类随机森林(RF)模型,对动力电池的安全风险进行分级预警。研究结果表明:该模型对电池多个参数具有很强的预测能力,如预测1 min后单体电压最高值的均方根误差(RMSE)为0.027 1,温度最高值为0.054 0;对电池系统1 min后安全风险等级预测的查准率为84%,宏平均f₁分数为74%。

关键词: 机器学习, 电动汽车, 电池系统, 风险预警, 样本卷积和交互网络(SCINet), 随机森林(RF)

Abstract:

In order to improve the timeliness and accuracy of safety early warning of power battery under real vehicle conditions, the safe early warning of the battery system was refined into two scientific problems: key state prediction and early warning classification based on predicted state. According to the battery state data in the real vehicle operation, the maximum value of single cell voltage and the range of the cell voltage were selected as the key prediction objects. Fisher scoring and Maximum Information Coefficient (MIC) were used to realize key feature selection, and Sample Convolution and Interaction Network model (SCINet) were used for key state prediction. Then, based on the predicted state, a multi-classification RF model was established to classify the safety risks of power batteries. The results show that the proposed model has a strong predictive ability for multiple parametrs of the battery. For example, the root mean square error (RMSE) of the highest cell voltage is 0.027 1 and the highest temperature is 0.054 0 after 1 min of prediction. The prediction accuracy of the safety risk level of the battery system after 1 min is 84%, and the macro-average f₁ score is 74%.

Key words: machine learning, electric vehicle, battery system, risk early warning, sample convolution and interaction network(SCINet), random forest (RF)

何淑波, 项薇, 石钟淼. 基于机器学习的电动汽车电池系统的风险预警[J]. 中国安全科学学报, 2023, 33(2): 159-165.

HE Shubo, XIANG Wei, SHI Zhongmiao. Risk early warning of electric vehicle battery system based on machine learning[J]. China Safety Science Journal, 2023, 33(2): 159-165.

图/表 10

图1

表1

表2

图2

图3

表3

图4

图5

表4

表5

参考文献 10

[1]	CHEN Siyan, GAO Zhenghai, SUN Tianjun. Safety challenges and safety measures of Li-ion batteries[J]. Energy Science & Engineering, 2021, 9(9): 1647-1672.
[2]	黄强, 陶风波, 刘洋, 等. 气液灭火剂对磷酸铁锂电池模组灭火能效研究[J]. 中国安全科学学报, 2020, 30(3): 53-59. doi: 10.16265/j.cnki.issn1003-3033.2020.03.009
	HUANG Qiang, TAO Fengbo, LIU Yang, et al. Study on performance of gas-liquid extinguishing agent for lithium iron phosphate battery modules[J]. China Safety Science Journal, 2020, 30(3): 53-59. doi: 10.16265/j.cnki.issn1003-3033.2020.03.009
[3]	刘同宇, 李师, 付卫东, 等. 大容量磷酸铁锂动力电池热失控预警策略研究[J]. 中国安全科学学报, 2021, 31(11): 120-126. doi: 10.16265/j.cnki.issn 1003-3033.2021.11.017
	LIU Tongyu, LI Shi, FU Weidong, et al. Study on early warning strategy of large LFP traction battery's thermal runaway[J]. China Safety Science Journal, 2021, 31(11): 120-126. doi: 10.16265/j.cnki.issn 1003-3033.2021.11.017
[4]	FENG Fei, HU Xiaosong, HU Lin, et al. Propagation mechanisms and diagnosis of parameter inconsistency within Li-ion battery packs[J]. Renewable and Sustainable Energy Reviews, 2019, 112(2):102-113. doi: 10.1016/j.rser.2019.05.042
[5]	LI Da, ZHANG Zhaosheng, LIU Peng, et al. Battery fault diagnosis for electric vehicles based on voltage abnormality by combining the long short-term memory neural network and the equivalent circuit model[J]. IEEE Transactions on Power Electronics, 2020, 36(2): 1303-1315. doi: 10.1109/TPEL.63
[6]	HONG Jichao, WANG Zhenpo, YAO Yongtao. Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks[J]. Applied Energy, 2019, 251:DOI: 10.1016/j.apenergy.2019.113381. doi: 10.1016/j.apenergy.2019.113381
[7]	ZHANG Liang, GAO Tian, CAI Guowei, et al. Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm[J]. Journal of Energy Storage, 2022, 49:DOI: 10.1016/j.est.2022.104092. doi: 10.1016/j.est.2022.104092
[8]	GAN Min, ZHANG Li. Iteratively local fisher score for feature selection[J]. Applied Intelligence, 2021, 51(8): 6167-6181. doi: 10.1007/s10489-020-02141-0
[9]	ZHENG Kangfeng, WANG Xiujuan, WU Bin, et al. Feature subset selection combining maximal information entropy and maximal information coefficient[J]. Applied Intelligence, 2019, 50(2): 487-501. doi: 10.1007/s10489-019-01537-x
[10]	LIU Minhao, ZENG Ailing, CHEN Muxi, et al. Time series is a special sequence: forecasting with sample convolution and interaction[J]. arXiv Preprint arXiv, 2021:DOI: arxiv-2106.09305. doi: arxiv-2106.09305

风险等级	表示	影响
0	无故障	正常
1	1级故障	不影响车辆正常行驶
2	2级故障	影响车辆性能,驾驶员限制行驶
3	3级故障	应立即停车处理或请求救援

编号	名称	编号	名称
A1	充放电时长	A7	最低温度值
A2	总电压	A8	SOC变化值
A3	总电流	A9	单体温度极差
A4	单体电压最高值	A10	单体电压极差
A5	最高温度值	A11	SOC
A6	单体电压最低值

特征	Informer	SSA-LSTM	SCINet
总电压	10.765 0	9.633 6	7.913 1
最高温度值	1.685 9	0.855 9	0.556 7
最低温度值	1.854 9	0.811 1	0.515 6
SOC变化值	13.903 0	12.211 0	9.602 8
温度极差	1.312 1	0.817 1	0.496 6
单体电压极差	0.059 0	0.039 9	0.032 7
单体电压最高值	0.090 1	0.072 7	0.027 1
单体电压最低值	0.084 0	0.066 6	0.054 0

算法	P	R	F₁	R_A
RF	0.84	0.66	0.74	0.79
KNN	0.56	0.67	0.60	0.78
DEC	0.77	0.63	0.69	0.77
GBDT	0.57	0.65	0.61	0.80

时长	P	R	F₁
第4步	0.84	0.66	0.74
第8步	0.80	0.57	0.67
第12步	0.75	0.53	0.62

基于机器学习的电动汽车电池系统的风险预警

Risk early warning of electric vehicle battery system based on machine learning

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 10

相关文章 15

编辑推荐

Metrics

本文评价

[1]	周成, 张相炎, 居里锴. 机械安全风险预警监测要素确定方法[J]. 中国安全科学学报, 2023, 33(2): 132-139.
[2]	朱彤, 秦丹, 魏雯, 任杰, 冯移冬. 基于机器学习的公交驾驶员事故风险识别及影响因素研究[J]. 中国安全科学学报, 2023, 33(2): 23-30.
[3]	陈永强, 吴志鹏, 杨湘娟, 陈璞, 孙树立. 区域地震分析软件研究综述[J]. 中国安全科学学报, 2022, 32(9): 126-136.
[4]	朱馨, 李建微, 郭伟, 毕胜, 伍跃飞. 基于机器学习的森林火险预测模型[J]. 中国安全科学学报, 2022, 32(9): 152-157.
[5]	吴贤国, 冯宗宝, 刘俊, 王雷, 陈虹宇, 李昕懿. 基于RF-NSGA-II的盾构施工地表沉降安全控制多目标优化*[J]. 中国安全科学学报, 2022, 32(8): 45-51.
[6]	张凯, 张科. 基于LightGBM算法的边坡稳定性预测研究[J]. 中国安全科学学报, 2022, 32(7): 113-120.
[7]	刘坤, 焦钰博, 张晓明, 陈晓宇, 蒋朝哲. 基于心电的铁路列车驾驶压力检测研究[J]. 中国安全科学学报, 2022, 32(6): 31-37.
[8]	刘全义, 朱博, 邓力, 石航, 梁光华. 基于机器学习的双参数火灾探测方法[J]. 中国安全科学学报, 2022, 32(5): 90-96.
[9]	杨文臣, 周燕宁, 田毕江, 郭凤香, 胡澄宇. 基于聚类分析和SVM的二级公路交通事故严重度预测[J]. 中国安全科学学报, 2022, 32(5): 163-169.
[10]	丁茜, 赵晓东, 吴鑫俊, 张泰丽, 徐振涛. 基于RBF核的多分类SVM滑塌易发性评价模型[J]. 中国安全科学学报, 2022, 32(3): 194-200.
[11]	张利冬, 宋泽阳, 罗振敏, 赵珊珊. 基于机器学习的煤自然发火期预测[J]. 中国安全科学学报, 2022, 32(12): 118-124.
[12]	张进春, 陈昕, 张文俊, 吴士龙. 基于广义极值分布的流化床压力波动风险预警[J]. 中国安全科学学报, 2022, 32(1): 85-91.
[13]	梁新苗, 肖凌云, 王澎, 董红磊, 曲现国, 刘川. 数据挖掘与现场调查结合的电动汽车事故分析[J]. 中国安全科学学报, 2022, 32(1): 180-187.
[14]	张洪涛, 李光华, 谢凤祥, 韩名亮. 车联网在水电企业行车安全管理变革中的应用[J]. 中国安全科学学报, 2021, 31(S1): 149-153.
[15]	吴仁彪, 何宇翔, 贾云飞. 基于改进层次分析法的重点人员风险评价方法^*[J]. 中国安全科学学报, 2021, 31(10): 112-118.